arXiv: cs.AI preprintsInternational6 November 2026
TSBPO: Tree-Structured Branching for Policy Optimization in Interactive Language Agents
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arXiv:2610.41697v2 Announce Type: replace Abstract: Terminal feedback in multi-turn agent training leaves both rollout allocation and local credit underdetermined. Dynamics-Predictive Sampling (DPS) [9] provides a task-level predictive sampling reference, but interactive agents also need to choose which visited states merit further continuations. We introduce TSBPO with two method components. A lightweight, fine-grained Root/Branch budget allocator distributes factual rollouts across tasks and additional suffixes across replayable states using likelihood-fitted estimates of terminal-outcome co
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